Obesity is one of the most prevalent health conditions with about 30% of the world's and over 70% of the United States' adult population being either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. (Ng, M., Fleming, T., Robinson, M., Thomson, B., Graetz, N., Margono, C., Mullany, E. C., Biryukov, S., Abbafati, C., Abera, S. F., et al.: Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the global burden of disease study 2013. The Lancet 384(9945), 766-781 (2014)) Central obesity, also known as abdominal obesity, is the excessive build-up of fat around stomach and abdomen. Central obesity has been held responsible for high levels of LDL cholesterol and triglycerides and lower levels of HDL cholesterol. There is a correlation between central obesity and disorders such as cardiovascular disease, heart attacks, strokes, high blood pressure, cancer, diabetes, osteoarthritis, fatty liver disease, metabolic syndrome and depression.
A simple method of assessing abdominal obesity is the waist circumference measurement. Generally, a waist circumference measurement above 88 cm for women and above 102 cm for men indicates abdominal obesity. However, this method does not take into account several important variables which should be considered such as the type of fat as well as the location of the abdominal fat. In addition, this method is more prone to errors in measurement and thus inaccuracies.
Another way to assess abdominal obesity is using a waist to hip ratio in which the waist measurement is divided by the hip measurement. The chance of a heart attack or stroke steadily increases as the ratio rises above 0.95 in men and 0.85 in women. Similar to only waist circumference measurements, this method is prone to errors in measurements which lead to inaccuracies.
Traditionally, Body Mass Index (BMI) has been used as a measure of obesity and metabolic health. BMI is the body mass (weight) divided by the square of the body height and is expressed in units of kg/m2. Generally accepted ranges include: under 18.5 kg/m2=underweight; 18.5 to 25 kg/m2=normal; 25 to 30 kg/m2=overweight; and over 30 kg/m2=obese. One problem with BMI measurement is that it remains inconsistent across subjects, especially for underweight, obese and highly muscular individuals. BMI also reflects only total body fat without regard to fat distribution.
Volumetry of abdominal fat is considered a reliable, accurate, and consistent measure of body fat distribution. Because visceral adipose tissue (VAT) manifests itself mainly in abdominal region, it is considered an important marker for evaluating central obesity thus making quantification of VAT vital for precise diagnosis and timely treatment of numerous diseases such as heart attacks, diabetes and cancer. VAT drains directly through portal circulation directly into the liver. VAT releases several bioactive molecules and hormones, such as adiponectin, leptin, tumour necrosis factor, resistin and interleukin 6 (IL-6) which are related to elevated glucose levels, hypertension, cardiovascular disease and other malignancies. In clinical literature, the association between VAT and different diseases has been thoroughly discussed. For instance, visceral obesity quantified through Computed Tomography (CT) was found to be a significant risk factor for prostate cancer. (Von Hafe, P., Pina, F., Pérez, A., Tavares, M., Barros, H.: Visceral Fat Accumulation as a Risk Factor for Prostate Cancer. Obesity 12(12), 1930 (2004)). Visceral adiposity has been found to be a significant predictor of disease-free survival rate in resectable colorectal cancer patients. (Moon, H. G., Ju, Y. T., Jeong, C. Y., Jung, E. J., Lee, Y. J., Hong, S. C., Ha, W. S., Park, S. T., Choi, S. K.: Visceral Obesity may affect Oncologic Out-come in patients with Colorectal Cancer. Annals of Surgical Oncology 15(7), 1918-1922 (2008)). In contrast to Subcutaneous Adipose Tissue (SAT), VAT was concluded to have an association with incident cardiovascular disease and cancer after adjustment for clinical risk factors and general obesity. (Britton, K. A., Massaro, J. M., Murabito, J. M., Kreger, B. E., Hoffmann, U., Fox, C. S.: Body Fat Distribution, Incident Cardiovascular Disease, Cancer, and All-Cause Mortality. Journal of the American College of Cardiology 62(10), 921-925 (2013)). Speliotes et al. found VAT as the strongest correlate of fatty liver among all the other factors used in their study. (Speliotes, E. K., Massaro, J. M., Hoffmann, U., Vasan, R. S., Meigs, J. B., Sahani, D. V., Hirschhorn, J. N., O'Donnell, C. J., Fox, C. S.: Fatty liver is associated with dyslipidemia and dysglycemia independent of visceral fat: the Framingham Heart Study. Hepatology 51(6), 1979-1987 (2010)). VAT was found to be an independent predictor of all-cause mortality in men after adjustment for abdominal subcutaneous and liver fat. (Kuk, J. L., Katzmarzyk, P. T., Nichaman, M. Z., Church, T. S., Blair, S. N., Ross, R.: Visceral fat is an independent predictor of all-cause mortality in men. Obesity 14(2), 336-341 (2006)). All these clinical evidences show that the robust and accurate quantification of VAT can help improve identification of risk factors, prognosis, and long-term health outcomes.
Subcutaneous adipose tissue (SAT), on the other hand, does not seem to be associated with increases in risk factors for the same diseases associated with higher VAT. In fact, some studies have observed a potential beneficial role for SAT noting that subjects having increased hip and thigh fat mass have lower glucose and lipid levels independent of abdominal fat. (Porter, S. A. et al., Abdominal subcutaneous adipose tissue: a protective fat depot?, Diabetes Care, 2009, 32(6): 1068-1075).
Unfortunately, it is difficult to automatically separate VAT from subcutaneous adipose tissue (SAT) because both VAT and SAT regions share similar intensity characteristics, similar Hounsfield unit (HU) in computerized tomography (CT), and are vastly connected. (FIG. 1B) Currently, to segregate these two fat types, radiologists usually use different morphological operations as well as manual interactions, however this process is subjective and not attractive in routine evaluations. Therefore, a set of representative slices at the umbilical level are often used for quantifying central obesity. (Tong, Y., Udupa, J. K., Torigian, D. A.: Optimization of abdominal fat quantification on CT imaging through use of standardized anatomic space: A novel approach. Medical physics 41(6), 063501 (2014)). However, these selections do not infer volumetric quantification. As such, inefficient and inaccurate quantification remains a major problem in clinical evaluation of central obesity and body fat distribution.
Brown adipose tissue (BAT), commonly known as a brown fat, and white adipose tissue (WAT) are two types of adipose tissue found in mammals. (FIG. 1A) Quantification of white adipose tissue and its subtypes is an important task in clinical evaluation of several conditions such as obesity, cardiac diseases, diabetes and other metabolic syndromes. BAT quantification studies are mostly based on qualitative observation of expert radiologists and nuclear medicine physicians since there is no automated CAD system available for this purpose. In those studies, after strictly chosen specific anatomical locations are explored for BAT presence, the quantification process is conducted either by manual or semi-automated delineation methods. (Muzik, O., Mangner, T. J., Leonard, W. R., Kumar, A., Janisse, J., Granneman, J. G.: 15o pet measurement of blood flow and oxygen consumption in cold-activated human brown fat. Journal of Nuclear Medicine 54(4), 523-531 (2013); Cohade, C., Osman, M., Pannu, H., Wahl, R.: Uptake in supraclavicular area fat (“usa-fat”): description on 18f-fdg pet/ct. Journal of Nuclear Medicine 44(2), 170-176 (2003))
It was recently found that there is an inverse relationship between BAT activity and body fatness which may suggest that BAT is protective against body fat accumulation because of its energy dissipating activity thus making BAT a potential target for combating human obesity and related metabolic disorders. (Saito, M., Brown adipose tissue as a therapeutic target for human obesity, Obesity Research & Clinical Practice, 2013, Vol. 7, Issue 6, pp. e432-e438).
Since PET images have high contrast, thresholding and/or clustering based methods are well suited for delineation of uptake regions. Simple thresholding is used for segmenting the uptake region pertaining to the BATs for extracting metabolic BAT volume and standardized uptake value (SUV) based metrics. BAT is considered present if there are areas of tissue that are more than 5 mm in diameter; there is a CT density of between −190 to −30 Hounsfield Units (HU); and there is an SUV of 18F-FDG of at least 2. Region of interests (ROIs) are used to manually remove false positive (FP) regions from consideration. There may be further manual FP removal steps for differentiating uptake between BAT regions and lymph nodes, vessels, bones, and the thyroid. (Gilsanz, V., Chung, S. A., Jackson, H., Dorey, F. J., Hu, H. H.: Functional Brown Adipose Tissue is Related to Muscle Volume in Children and Adolescents. The Journal of pediatrics pp. 722-726 (2011)) Each of these manual identifications require extensive user knowledge of the anatomy and hence are prone to errors. Furthermore, in case of existence of pathologies, segregating pathologies from normal variants of 18F-FDG or BAT regions can be extremely challenging.
BATs are important for thermogenesis, and are considered as natural defense against hypothermia and obesity. (Cypess, A. M., Lehman, S., Williams, G., Tal, I., Rodman, D., Goldfine, A. B., Kuo, F. C., Palmer, E. L., Tseng, Y. H., Doria, A., Kolodny, G. M., Kahn, C. R.: Identification and importance of brown adipose tissue in adult humans. New England Journal of Medicine 360(15), 1509-1517 (2009)) In contrast to WAT, BATs are metabolically active, so functional imaging modalities can help in detecting these tissues. In this regard, sensitivity of Positron Emission Tomography (PET) imaging is much higher than that of magnetic resonance imaging (MRI) and computed tomography (CT) for visualizing and quantifying BATs. (FIG. 1C) However, PET lacks specificity due to limited structural information. When combined with CT and/or MRI, both specificity and sensitivity are increased due to incorporation of anatomical sites into the evaluation framework. Despite rapid improvements in the imaging facets of BAT detection, the available methods are limited to manual and semi-automated strategies; hence, they are time-consuming and non-reproducible.
Previous Work
Body fat quantification has been a long-time active area of research for medical imaging scientists. For abdominal fat (central obesity) quantification, Zhao et al. used intensity profile along the radii connecting sparse points on the outer wall (skin boundary) starting from the abdominal body center. (Zhao, B., Colville, J., Kalaigian, J., Curran, S., Jiang, L., Kijewski, P., Schwartz, L. H.: Automated quantification of body fat distribution on volumetric computed tomography. Journal of computer assisted tomography 30(5), 777-783 (2006)) Boundary contour is then refined by a smoothness constraint to separate VAT from SAT. This method, however, does not adapt to obese patients easily where the neighboring subcutaneous and/or visceral fat cavities lead to leakage in segmentation.
In another study, Romero et al. developed different search strategies based on heuristics to generate the abdominal wall mask on a small set of representative slices. However, this method is prone to inefficiencies for subjects in which the abdominal wall is too sparse. (Romero, D., Ramirez, J. C., M'armol, A.: Quantification of subcutaneous and visceral adipose tissue using ct. In: Medical Measurement and Applications, 2006. MeMea 2006. IEEE International Workshop on. pp. 128-133. IEEE (2006))
In a similar fashion, Pednekar describes a method based on a hierarchical fuzzy affinity function derived semi-supervised segmentation. As the method uses about half of its experimental data for training, its success was vague and dependent on the selection of training subjects especially when patient specific quantification is considered. (Pednekar, A., Bandekar, A. N., Kakadiaris, I., Naghavi, M., et al.: Automatic segmentation of abdominal fat from ct data. In: WACV 2005. vol. 1, pp. 308-315. IEEE (2005))
Mensink et al. proposed a series of morphological operations, however fine tuning of the algorithm was difficult for patient specific quantification, and this fine tuning would have to be repeated almost for every patient when the abdominal wall is too thin. (Mensink, S. D., Spliethoff, J. W., Belder, R., Klaase, J. M., Bezooijen, R., Slump, C. H.: Development of automated quantification of visceral and subcutaneous adipose tissue volumes from abdominal ct scans. In: SPIE Medical Imaging. pp. 79632Q-79632Q. International Society for Optics and Photonics (2011))
More recently, Kim et al. generated subcutaneous fat mask using a modified “AND” operation on four different directed masks with some success shown. However, logical and morphological operations make the whole quantification system vulnerable to inefficiencies. (Kim, Y. J., Lee, S. H., Kim, T. Y., Park, J. Y., Choi, S. H., Kim, K. G.: Body fat assessment method using ct images with separation mask algorithm. Journal of digital imaging 26(2), 155-162 (2013))
A more advanced method was presented by Chung in which SAT, VAT and muscle are separated using a joint shape and appearance model, however the reproducibility of the method is highly dependent on the model at hand. (Chung, H., Cobzas, D., Birdsell, L., Lieffers, J., Baracos, V. Automated segmentation of muscle and adipose tissue on ct images for human body composition analysis. In: SPIE Medical Imaging. pp. 72610K-72610K. International Society for Optics and Photonics (2009))
Based on a similar idea as in Zhou, a recent method by Kim et al. estimated the muscle boundary using a convex-hull and then performed smoothing by selecting points that minimize the distance between the contour and the organ regions. However, the performance is dependent on the goodness of fit of the convex-hull. Although the method addresses SAT-VAT separation at a volumetric level, it lacks the use of important appearance features and volumetric smoothing. (Kim, Y. J., Park, J. W., Kim, J. W., Park, C. S., Gonzalez, J. P. S., Lee, S. H., Kim, K. G., Oh, J. H.: Computerized Automated Quantification of Subcutaneous and Visceral Adipose Tissue From Computed Tomography Scans: Development and Validation Study. Journal of Medical Internet Research; Medical Informatics 4(1) (2016))
Recently, work has been done by Gifford et al. in which PET/CT scans are used to automatically generate a BAT mask, which is then applied to co-registered MRI scans of the patient thus enabling measurement of quantitative MRI properties of BAT without manual segmentation. (Gifford, A. et al., Human brown adipose tissue depots automatically segmented by positron emission tomography/computed tomography and registered magnetic resonance images, 2015, Journal of Visualized Experiments, 96:e52415) This approach differs from the approach described herein by the use of both PET/CT and MRI which requires four imaging visits from the patient. Similar to all other approaches, BAT masks were generated by using SUV and HU information jointly such that fat regions are defined manually in CT images and this step is followed by checking the SUV's of corresponding pixels in PET images, if higher values are observed, BAT is considered for that pixel. Unfortunately, this procedure does not optimize the BAT region definition as it only includes sub-optimal thresholding and does not access the existence of abnormalities in contrast to the approach described herein.
Shi et al. describe a robust two-stage VAT/SAT separation framework for CT data in which adipose tissue is distinguished from other tissue types through a robust mixture of Gaussian model after which spatial recognition relevant to anatomical locations is used to differentiate between visceral and subcutaneous adipose tissue. (Shi et al., Robust separation of visceral and subcutaneous adipose tissues in micro-CT of mice, 2013, 35th Annual International Conference of IEEE EMBS, pp. 2312-2315) The Shi et al. approach has the disadvantage of being tested only on small animal images where internal organs are visualized with better spatial resolution, and parameters of the methods are easier to tune. In the low-resolution, non-contrast CT images, that are used for human cases, it is extremely difficult to set parameters. Shi et al. does not provide any evidence of being used in human CT scans and the approach would likely not be successful in humans since the approach is not data-driven and thus cannot account for personalized differences such as anatomical variations (different BMIs, etc.) or pathology presence (tumors, bone cracks, etc.). In contrast, the approach described herein is data-driven, easily adjusting for different personalized parameters of each patient.
There has not been an automated Computer-Aided Detection (CAD) system proposed for BAT quantification using radiology scans. Existing studies are mostly based on the qualitative observations of expert radiologists and nuclear medicine physicians. In those studies, strictly chosen specific anatomical locations were explored for BAT presence. (Muzik, O., Mangner, T. J., Leonard, W. R., Kumar, A., Janisse, J., Granneman, J. G.: 15 O PET Measurement of Blood Flow and Oxygen Consumption in Cold-Activated Human Brown Fat. Journal of Nuclear Medicine 54(4), 523-531 (2013); Cohade, C., Osman, M., Pannu, H., Wahl, R.: Uptake in supraclavicular area fat (“USA-Fat”): Description on 18 F-FDG PET/CT. Journal of Nuclear Medicine 44(2), 170-176 (2003)). The quantification process was conducted either by manual or semi-automated delineation methods. Since PET images have high contrast, thresholding and clustering-based methods are well-suited for the delineation of uptake regions. Therefore, a simple thresholding was often used for segmenting uptake regions pertaining to BAT, allowing the extraction of volumetric and S U V (i.e., “standardized uptake value”) based metrics. BAT is considered present if there are areas of tissues that are (i) more than 5 mm in diameter, (ii) CT density is restricted to −190 to −30 Hounsfield Units (HU), and (iii) have an SUV of 18F-fluorodeoxyglucose (18F-FDG) of at least 2 g/ml in corresponding PET images. Here it is important to note that in Baba, the authors chose the thresholding value for SU Vmax>3 g/ml to identify BAT regions. (Baba, S., Jacene, H. A., Engles, J. M., Honda, H., Wahl, R. L.: CT Hounsfield Units of Brown Adipose Tissue Increase with Activation: Preclinical and Clinical Studies. Journal of Nuclear Medicine 51(2), 246-250 (2010)). Hence, there is no clear consensus on the choice of SUV for BAT regions. In the last step, regions of interest (ROIs) are manually defined to remove false positive (FP) regions from consideration. Several manual FP removal steps may be required for differentiating uptake between BAT regions and lymph nodes, vessels, bones, and the thyroid. (Gilsanz, V., Chung, S. A., Jackson, H., Dorey, F. J., Hu, H. H.: Functional Brown Adipose Tissue is Related to Muscle Volume in Children and Adolescents. The Journal of Pediatrics pp. 722-726 (2011)). All these manual identifications require extensive user knowledge of the anatomy. Furthermore, in cases where pathologies are present, segregating pathologies from normal variants of 18F-FDG on BAT regions can be extremely challenging. (Cypess, A. M., Lehman, S., Williams, G., Tal, I., Rodman, D., Goldfine, A. B., Kuo, F. C., Palmer, E. L., Tseng, Y. H., Doria, A., Kolodny, G. M., Kahn, C. R.: Identification and Importance of Brown Adipose Tissue in Adult Humans. New England Journal of Medicine 360(15), 1509-1517 (2009)).
In light of the shortcomings of the current approaches, what is needed is a way to automate the detection, segmentation and quantification of SAT, VAT and BAT regions.